Three lines of code. Persistent AI memory.

from hebbrix import MemoryClient

async with MemoryClient(api_key="YOUR_API_KEY") as mem:
    # Store something your agent learned
    await mem.memories.create(
        content="User prefers Python, concise answers",
        collection_id="user-alex-123"
    )

    # Retrieve relevant context before a call
    results = await mem.search(
        "user preferences",
        collection_id="user-alex-123"
    )
# Knowledge graph, 5-layer search,
# and auto-RL happen behind the scenes.

import openai

# Change two lines. Keep everything else.
client = openai.OpenAI(
    base_url="https://api.hebbrix.com/v1",
    api_key="your_hebbrix_key"
)

response = client.chat.completions.create(
    model="gpt-4",
    messages=[{
        "role": "user",
        "content": "What are my preferences?"
    }]
)
# Hebbrix searched memories and injected
# context before forwarding to GPT-4.